23.8 卷积 LSTM
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小牛编辑
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2023-12-01
该网络用于预测包含移动方块的人工生成的电影的下一帧。
from keras.models import Sequential
from keras.layers.convolutional import Conv3D
from keras.layers.convolutional_recurrent import ConvLSTM2D
from keras.layers.normalization import BatchNormalization
import numpy as np
import pylab as plt
# 我们创建一个网络层,以尺寸为 (n_frames,width,height,channels) 的电影作为输入,并返回相同尺寸的电影。
seq = Sequential()
seq.add(ConvLSTM2D(filters=40, kernel_size=(3, 3),
input_shape=(None, 40, 40, 1),
padding='same', return_sequences=True))
seq.add(BatchNormalization())
seq.add(ConvLSTM2D(filters=40, kernel_size=(3, 3),
padding='same', return_sequences=True))
seq.add(BatchNormalization())
seq.add(ConvLSTM2D(filters=40, kernel_size=(3, 3),
padding='same', return_sequences=True))
seq.add(BatchNormalization())
seq.add(ConvLSTM2D(filters=40, kernel_size=(3, 3),
padding='same', return_sequences=True))
seq.add(BatchNormalization())
seq.add(Conv3D(filters=1, kernel_size=(3, 3, 3),
activation='sigmoid',
padding='same', data_format='channels_last'))
seq.compile(loss='binary_crossentropy', optimizer='adadelta')
# 人工数据生成:
# 生成内部有3到7个移动方块的电影。
# 方块的尺寸为 1x1 或 2x2 像素,
# 随着时间的推移线性移动。
# 为方便起见,我们首先创建宽度和高度较大的电影(80x80),最后选择 40x40 的窗口。
def generate_movies(n_samples=1200, n_frames=15):
row = 80
col = 80
noisy_movies = np.zeros((n_samples, n_frames, row, col, 1), dtype=np.float)
shifted_movies = np.zeros((n_samples, n_frames, row, col, 1),
dtype=np.float)
for i in range(n_samples):
# 添加 3 到 7 个移动方块
n = np.random.randint(3, 8)
for j in range(n):
# 初始位置
xstart = np.random.randint(20, 60)
ystart = np.random.randint(20, 60)
# 运动方向
directionx = np.random.randint(0, 3) - 1
directiony = np.random.randint(0, 3) - 1
# 方块尺寸
w = np.random.randint(2, 4)
for t in range(n_frames):
x_shift = xstart + directionx * t
y_shift = ystart + directiony * t
noisy_movies[i, t, x_shift - w: x_shift + w,
y_shift - w: y_shift + w, 0] += 1
# 通过添加噪音使其更加健壮。
# 这个想法是,如果在推理期间,像素的值不是一个,
# 我们需要训练更加健壮的网络,并仍然将其视为属于方块的像素。
if np.random.randint(0, 2):
noise_f = (-1)**np.random.randint(0, 2)
noisy_movies[i, t,
x_shift - w - 1: x_shift + w + 1,
y_shift - w - 1: y_shift + w + 1,
0] += noise_f * 0.1
# Shift the ground truth by 1
x_shift = xstart + directionx * (t + 1)
y_shift = ystart + directiony * (t + 1)
shifted_movies[i, t, x_shift - w: x_shift + w,
y_shift - w: y_shift + w, 0] += 1
# 裁剪为 40x40 窗口
noisy_movies = noisy_movies[::, ::, 20:60, 20:60, ::]
shifted_movies = shifted_movies[::, ::, 20:60, 20:60, ::]
noisy_movies[noisy_movies >= 1] = 1
shifted_movies[shifted_movies >= 1] = 1
return noisy_movies, shifted_movies
# 训练网络
noisy_movies, shifted_movies = generate_movies(n_samples=1200)
seq.fit(noisy_movies[:1000], shifted_movies[:1000], batch_size=10,
epochs=300, validation_split=0.05)
# 在一部电影上测试网络
# 用前 7 个位置训练它,然后预测新的位置
which = 1004
track = noisy_movies[which][:7, ::, ::, ::]
for j in range(16):
new_pos = seq.predict(track[np.newaxis, ::, ::, ::, ::])
new = new_pos[::, -1, ::, ::, ::]
track = np.concatenate((track, new), axis=0)
# 然后将预测与实际进行比较
track2 = noisy_movies[which][::, ::, ::, ::]
for i in range(15):
fig = plt.figure(figsize=(10, 5))
ax = fig.add_subplot(121)
if i >= 7:
ax.text(1, 3, 'Predictions !', fontsize=20, color='w')
else:
ax.text(1, 3, 'Initial trajectory', fontsize=20)
toplot = track[i, ::, ::, 0]
plt.imshow(toplot)
ax = fig.add_subplot(122)
plt.text(1, 3, 'Ground truth', fontsize=20)
toplot = track2[i, ::, ::, 0]
if i >= 2:
toplot = shifted_movies[which][i - 1, ::, ::, 0]
plt.imshow(toplot)
plt.savefig('%i_animate.png' % (i + 1))